Machine Learning For Beginners Guide Algorithms E-Book

William Sullivan

• Herausgeber: WS
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch
• Veröffentlichungsjahr: 2017

Seitenzahl: 170

Beschreibung

Machines can LEARN ?!?!

Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures

Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning

Note: Bonus chapters included inside!

Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? Its a worthwhile read and the most valuable investment you can make for yourself

Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available for beginners.

What You'll Learn

• Supervised Learning

• Unsupervised Learning

• Reinforced Learning

• Algorithms

• Decision Tree

• Random Forest

• Neural Networks

• Python

• Deep Learning

• And much, much more!

This is the most comprehensive and easy to read step by step guide in machine learning that exists.

Learn from one of the most reliable programmers alive and expert in the field

You do not want to miss out on this incredible offer!

Leseprobe

Machine Learning For Beginners Guide Algorithms:

Supervised & Unsupervised Learning Decision Tree & Random Forest Introduction

You Might Also Be Interested In..

––––––––

Introduction:

Chapter 1: About Machine Learning

What is Machine Learning?

History:

Chapter 2: Machine Learning Basics

Differences between Traditional Programming and Machine Learning

Elements of Machine Learning

Types and Kinds of Machine Learning

Machine Learning in Practice

Learning models

Sample applications of machine learning

Chapter 3: Machine Learning: Algorithms

Ensemble Learning Method

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

Algorithms Grouped By Similarity

Chapter 4: Decision Tree and Random Forests: Part One

What is a Decision Tree? How exactly does it work?

Decision Tree, Algorithms

Types of Decision Trees

Terminology and Jargon related to Decision Trees

Advantages

Disadvantages

Regression Trees vs. Classification Trees

Where does the tree get split?

Gini Index

Chi-Square

Information Gain, Decision Tree

Reduction in Variance

Chapter 5: Decision Trees: Part 2

Tree Pruning

Linear models or tree based models?

Ensemble methods:

What is Bagging? How does it work?

Chapter 6: Decision Trees: Part Three (Random Forests)

Workings of Random Forest:

Advantages of Random Forest

Disadvantages of Random Forest

What is Boosting? How does it work?

By utilizing average or weighted average

How do we choose a different distribution for each round?

GBM or XGBoost: Which is more powerful?

How to work with GBM in R and Python?

Chapter 7: Deep Learning

The difference between Machine Learning, Deep Learning, and AI:

Chapter 8: Digital Neural Network and Computer Science

Applications of ANN

Advantages of ANN

Risks associated with ANN

Types of Artificial Neural Networks

Conclusion

© Healthy Pragmatic Solutions Inc. Copyright 2017 - All rights reserved.

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Please note the information contained within this document is for educational purposes only. No warranties of any kind are expressed or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical or professional advice. Please consult a licensed professional before attempting any techniques outlined in this book.

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About The Series

"Machine Learning For Beginners Guide Algorithms" is the first installment in this book series, meticulously developed by me and my passionate software loving engineering team.

This series will provide you in depth insights and a full introduction to the world of machine learning.

Whenever we cover a new concept, topic or formula I ensure that a full in depth explanation is provided and push your understanding of the modern technology to a whole new level. Diagrams are provided to help maximize and visualize concepts,  enhancing the learning process.

Please understand that this series will challenge your way of thinking. Especially, in later books we will dive into extremely technical topics that will inspire you.

I just need three things from you before we can begin. Please stay committed, focused and passionate throughout the duration of all subject matter.

I have worked tirelessly structuring all this content together in the most practical, easy to read and step by step guide. I try to keep "high tech jargon" to a minimal and keep the flow of reading seamless and uninterrupted.

This is the first publication officially released to the public - stay tuned for the newest releases by following my author page or simply find the author page directly under the book on Amazon.com

Feel free to comment and give feedback on potential new topics you'd like to learn about. I gather all input given by readers and take it into serious consideration when writing a new book.

Whenever you are ready, let's dive into the world of machine learning together! Turn the page. :)

Introduction

I want to thank you for choosing this book, ‘Machine learning for beginners - Algorithms, Decision Tree & Random Forest Introduction.’

By choosing this book, you have made the right decision, as you will learn many new, innovative and exciting things about the world of technology and computers.  It will help you learn the basics of AI and machine learning in a simple, entertaining and informative way.

Currently one of the most talked about topics in the world of technology, machine learning is a promising concept. But along with the promises and benefits, it is also often associated with controversies and debates. People who are not aware of the nature and advantages of machine learning or have received their information from untrustworthy sources often look down on machine learning and are scared of it as well. However, all the strange and bizarre things that you have heard about machine learning are probably just myths and false apprehensions.

This book will try to do away with such apprehensions by showing how machine learning is perhaps the best thing that could happen to the world of technology right now.  You will get answers to all your questions about machine learning and more. So, rather than making assumptions, you will learn and understand what machine learning is all about and make your own decisions.

So let’s read on.

Chapter 1: About Machine Learning

One of the best features of today’s era of technology is its flexibility and adaptableness. A new scientific innovation comes out almost every day. This ever-changing nature of scientific and technological world changes the trajectory of the world every day. Things that were considered dreams and fiction once are now rapidly turning into reality. Human beings are slowly but steadily trying to defeat nature at its own game. However, one field remains to be conquered. We still have not managed to conquer the world of machine learning or AI. However, it has become a buzzword now, and the whole of the world is talking about it.  Not everyone is excited about it though. Most people are worried or scared of it. However, there is no need to be afraid of machine learning or AI, as it will help humanity to achieve things that we cannot even currently imagine.

What is Machine Learning?

If you check the search results for the most popular keywords of 2016, you will find that machine learning and AI are leading the figures by a large margin. This steady rise in the fame of machine learning is because of its rising use in our daily lives. It is nowadays being used in various devices and machines as well as gadgets. However, the general population is still are wary of it. So, to do away with such myths, let us have a look at the brief history of machine learning.

As per the 1959 definition of Arthur Samuel, machine learning can be defined as a process of inputting data to the computer systems in a way that the computer will learn the ability to process and perform the activity in the future without being explicitly programmed or being fed with similar or extra data. What this means in simple words is that it will allow computers to develop a ‘mind’ of their own and allow them to “think.” Sounds scary but it isn’t.

If computers are provided with the ability to think, they become smarter and thus easier to use. Their functionality will increase by a large margin, and they become an integral asset for humanity. Machine learning can be used in almost all the fields of epistemology. Right now, it is being used in areas such as cheminformatics, computational anatomy, gaming, adaptive websites, natural language processing, robot movement and locomotion, medical diagnosis, sequence mining, behavior analysis, linguistics, translation, fraud detection, etc. The list goes on.

History:

The history of machine learning can be traced to the birth of another related field- AI or artificial intelligence. It is safe to say that both of these fields were born at the same time and then got separated over time. Many scientists studying AI in the beginning slowly shifted towards studying machine learning academically.  They started using probabilistic reasoning etc. Around the ‘90s the two fields, AI and machine learning, were officially separated, and now both of them are studied individually.  In the following chapters, you will learn the basics of machine learning and how it can be used in day-to-day life. You will also learn about the careers that are available in this field as well as certain advanced topics for the experts.

Chapter 2: Machine Learning Basics

What is it that has made machine learning a buzzword in today’s era? The simplest answer to the above question would be its unique, feature-rich nature that can change the future of humanity forever. In the words of Bill Gates:

“A breakthrough in machine learning would be worth ten Microsofts.”

What the above statement roughly means is that scientists and computer experts all over are desperately looking for a breakthrough in machine learning and are looking for a way to make it more accessible, useful and trustworthy. However, such programs are still going, and we still haven’t found a way to devise a machine that could think.

In machine learning, computers learn to program themselves. If programming is considered to be automation and an automatic process, then machine learning is the automation of this automatic process, thus making a double automatic process.

Machine learning can make programming more scalable and can help us to produce better results in shorter durations. To prove this, let us see the following comparison:

Differences between Traditional Programming and Machine Learning

Traditional Programming:

The data is fed to the computer, and a program is run. This program then, using the supplied data, presents output.

Machine Learning:

Pre-solved data and the resulting output are fed to the computer. These two inputs are used to create a program. This program then can do the job of traditional programming.

Thus, machine learning can be explained by using the metaphors of agriculture. Algorithms are, in a way seeds while data is nutrients. You are the farmer while the program that grows out of the data is your crop.

Elements of Machine Learning

As machine learning is a complicated and convoluted field, it's hard to understand its basics. It is also an ever-growing field. Hence it is possible to see new development in the area almost every day. For instance, it is believed that every year more than a few hundred, new algorithms are developed all over the world. This brings the number of overall machine learning algorithms to a sum that is larger than ten thousand. Even though a lot of variety is seen in the algorithms of machine learning, all of them are based on three basic concepts that are as follows.

Representation:

This concept deals with the representation of knowledge. It deals with how the knowledge can be represented, what is necessary to represent the knowledge etc.  Some examples of representation include sets of rules, including decision trees, support vector machines, instances, neural networks, graphical models, model ensembles, etc. Some of these will be discussed in the book later.

Evaluation:

This is the second most important concept of algorithms. It is the way used to evaluate the hypotheses, also known as the candidate program.  Some examples are accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence and others.

Optimization:

This is the third and last concept of algorithms. It is the method in which the hypothesis or the candidate program is created. It is also known as the search process. Examples include combinatorial optimization, convex optimization and constrained optimization.

Making various combinations of the above components creates all machine-learning algorithms, and thus they are the basis of machine learning.

Types and Kinds of Machine Learning

As said earlier, machine learning is complex and vast field hence it can be divided into many sections and classes. However, on a superficial level, it can be split into four parts, and they are as follows:

Supervised Learning

Unsupervised Learning

Semi-supervised Learning

Reinforcement Learning

Supervised learning:

Supervised Learning is also known as inductive learning in the technological circles.  It is considered to be the most advanced and mature of all the forms of learning. This is why it is the most studied as well as most used learning as well. It is easy to used Learning type as it is much easier to learn under supervision than without supervision. In Inductive Learning, we are presented with an example of a function in the form of data (x), and the output of the function is (f(x)). The mission of inductive learning here is to understand and learn the function for the new data (x).

In this learning, the program is ‘trained’ with the help of some already defined set of ‘examples.' This training helps the program to learn the ability to formulate a new and accurate result using the newly fed data with ease and without any interference.

Supervised learning is the most used and most favorite of all forms of learning, this chapter as well as this book will try to focus on it. Other types will be discussed briefly.

In most of the supervised learning applications, the final mission is to create a proper and well-set predictor function h(x). It is also known as the hypothesis. The ‘learning’ contains many mathematical algorithms that are necessary to optimize the function. When it is optimized, it can correctly predict the value of h(x) if data X is fed to the computer related to a particular domain. For instance, if the data being fed is the square footage of agricultural land, the program should be able to return the estimated price for the piece of land.

However, it is seen that x always represents more than one data point. For instance, if we are to continue the above example, then the program may take the number of wells (x2), number of trees (x3), number of greenhouses (x4), number of electric poles (x5), number compost holes (x6) and many other variables along with the first one that is the square footage of the land (x1). Then the determination of the correct input is the input that will come out with the correct result. This is one of the major parts of machine learning design. However, as the topic might get too complex and complicated, this example will only assume a single input value.

Example

Let us assume that the program or predictor is using this form:

Here θ0 and θ1 are constants. The mission here is to find the perfect values for the above two constants to create and make our predictor work properly.

To optimize the predictor h (x), training examples are used. In each of these examples a value of x train is added and corresponding to this value an output value- y is already known. For instance, the difference between the known i.e. correct value y, and the predicted value h (x train) is found. When enough training examples are fed, the differences can be studied and checked to determine and measure our faults of h (x). Using our findings, we can change and manipulate the h (x) by manipulating the values of θ0 and θ1 to make it more accurate. This process then is repeated until the best values of θ0 and θ1 are found. This is how the predictor is trained. This trained predictor can now read real life data and predict perfectly to almost perfect results.

Unsupervised learning: 

The data that is fed to the system i.e. the training data does not include any desired output. Thus, the data that is to be fed is without output. It is difficult to understand and proclaim that whether this is good and recommended method of learning or not. Examples include clustering etc.

Semi-supervised learning: 

This is a mixture of both the above kinds of learning where training data contains some but not all desired outputs.

Reinforcement learning: 

Considered to be the most ambitious of all the types of learning. Here rewards are given from a sequence of actions. AI often prefers this.

Machine Learning in Practice

Although an important part of the overall machine learning process, machine algorithms are a tiny part of the complete process. In reality, the process is much more complicated. An example is as follows:

Start Loop

Here the domain is to be understood. The current data on hand, available knowledge and goals are analyzed. This often includes communicating with the domain experts. The goals are often unclear, and you have to attempt and try many things before implementing anything.

Data integration, selection, cleaning and pre-processing

This is the most time-consuming part in the overall process. It takes almost half or more than half of the overall required processing time. Procuring high-quality data is critical. However, the quality and quantity of data are often reversely proportional as the more the data, the more it will be dirty. Sorting out good and usable data from the rest is why this process takes a very long time.

Learning models

This part, though being the most mature part of the process, is also the most fun part of it. General tools are used for this.

Interpreting results

In many cases, it is not necessary to understand how the model works, the only focus being the results. Often human experts can challenge you on this.

Consolidating and deploying discovered knowledge.

Though many projects succeed in the lab and are remarkable, yet the chances of them being used in real life are quite rare. Most of the projects are discarded and not used in real life at all.

End Loop

There is an end cycle at the end, and it is not a one-shot process. It is necessary to run the loop until a desired and usable result is achieved.  The data can also change midway, affecting the overall process, as you need a new loop to replace the earlier one.

Sample applications of machine learning

This is a small list of fields in which machine learning can be applied to achieve great results:

●  Web search: It can rank web pages and search entries according to your previous clicks and likes and will prominently show new, similar results.

●  Computational biology:  Can rationally create drugs in the system or computer using old and past experiments.

●  Finance: Can evaluate how much risk is present on a credit card. Can also be used to send tailored offers according to the likings of a customer. Can also help you in choosing where to invest money.

●  E-commerce:  Identifying a transaction’s nature, whether it is a true and correct transaction or whether it is a fraudulent one.  It can also help you in predicting the number of customers.

●  Space exploration: Can help with radio astronomy and space probes.

●  Robotics: Can help to create self-driving cars and autonomous robots. Can contribute to building robots that can handle uncertain situations and new environments.

●  Information extraction: Can extract information from databases on the web.

●  Social networks: Can help you get data on preferences and relationships.

●  Debugging:  Can be used for debugging.

Chapter 3: Machine Learning: Algorithms

While discussing the basics of machine learning in the last chapter, we talked briefly about machine learning in algorithms. Let us take a deeper look at the algorithms, their types, the most popular ones and everything else about them in this chapter.

As said in the last chapter, the sheer number of algorithms that are available can overwhelm any beginner to machine learning. Therefore, it is necessary to categorize them in two large sections for our convenience.